In addition, we considered the cutout approach proposed in DeVries & Taylor (2017). This method randomly masks out contiguous square regions of the input images to CNNs, thereby augmenting the training data with partially occluded versions of existing samples. In particular, cutout applies a fixed-size zero-mask to a random portion of each input during each epoch. In this way, the technique encourages the neural network to take more of the full image context into account instead of relying on specific visual features. Thus, cutout improves robustness and yields higher accuracy levels [1]}.
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